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Earlier sources: Physics
This science thread follows physics through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
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Open in the map ↗316 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Robust collapse prediction for corroded subsea pipelines under multiaxial loading: A physics-informed Bayesian framework constrained by symbolic regression ↗
Subsea pipeline collapse under multiaxial loads and corrosion severely threatens offshore structural integrity. This study introduces an integrated FE-SR-PINN-BNN framework, combining detailed 3D finite element (FE) numerical modeling in ABAQUS with a physics-informed Bayesian neural network (PINN-BNN) constrained by symbolic regression (SR). Explicit algebraic expressions of underlying mechanical mechanisms derived via SR are embedded into the PINN-BNN loss function as structural physics constraints. Numerical results show that the proposed model achieves a high coefficient of determination (…
- OpenAlex · Published · First archived
A METHODOLOGICAL SYSTEM FOR PROFESSIONALLY ORIENTED PHYSICS TEACHING IN CHEMICAL TECHNOLOGY PROGRAMS: DESIGN AND EXPERIMENTAL VERIFICATION ↗
This article reports a study on the scientific and methodological basis of professionally oriented physics teaching for students of chemical engineering and technology at universities. The topic is relevant because industry, driven by digital transformation, Industry 4.0, intelligent manufacturing and the shift to a knowledge-based economy, now expects a new kind of engineer. Our analysis shows that the traditional physics course, built mainly around fundamental laws and standard problems, does not give students enough practice in applying physical principles to the analysis and design of tech…
- OpenAlex · Published · First archived
Sequence-Conditioned Physics-Informed Neural Network for Lithium-Ion Battery State of Health Estimation Using Charging Segments at High State of Charge ↗
Accurate and reliable state of health (SOH) estimation is essential for lithium-ion battery safety and lifetime management. Physics-informed neural networks (PINNs) integrate battery degradation knowledge with deep learning to improve the physical consistency of SOH estimation. However, existing PINNs insufficiently capture degradation evolution across consecutive cycles and show limited applicability across battery chemistries and charge/discharge protocols. To address these issues, we propose a sequence-conditioned physics-informed neural network (SC-PINN) that uses a gated recurrent unit (G…
- OpenAlex · Published · First archived
Real-time hydrogen gas explosion prediction from fuel cell vehicles by physics-informed graph neural network ↗
Hydrogen fuel cell vehicles (HFCVs) generate electricity from hydrogen via electrochemical reactions and offer an efficient, environmentally friendly pathway to the dual‑carbon targets. Nevertheless, hydrogen leakage can rapidly mix with air to form large flammable vapor clouds. In congested urban areas, the resulting vapor cloud explosions may cause severe structural damage and significant economic losses. Machine learning techniques have been developed to predict the evolution of gas explosions. Nevertheless, accurately capturing the spatial relationships among obstacles and predicting explo…
- OpenAlex · Published · First archived
Physics-guided data fusion for settlement prediction of high-fill railway embankments ↗
Settlement prediction for high-fill railway embankments is challenged by monitoring noise and stage-dependent deformation across construction and operation. This paper presents a physics-guided framework integrating multivariate construction information with settlement monitoring data. Monitoring sequences are denoised by a hybrid autoencoder with a signal-to-noise-ratio-based cleaning mechanism. A temporal graph convolutional neural network embeds a modified layer-wise summation formulation to guide variable interactions and settlement prediction. On the analysed dataset, the model achieved a…
- OpenAlex · Published · First archived
Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem ↗
We develop two parametric data-driven reduced models: a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order model (TROM), and apply both approaches to the parametrized one-dimensional shallow-water dam-break problem. Both reduced models do not require time integration and learn a direct solution map from space, time, and dam-break parameters to the physical state. We present a detailed comparison for out-of-sample and extrapolated parameter values. In addition, we demonstrate that it is essential to introduce shock-aware collocation to improve the robustness of t…
- arXiv · Published · First archived
An entangling Hubbard quasiparticle collider on a digital quantum processor ↗
Scattering of quasiparticles provides direct access to the interactions that govern strongly correlated systems. In this work, we realize the quasiparticle scattering experiment of the Fermi-Hubbard model on a digital quantum processor by colliding a doublon and a holon in the half-filled chain of 21 sites encoded on 42 qubits of the ibm_boston processor. For the first time, we measure the transmission probability of quasiparticles of the Hubbard model on quantum hardware, which agrees with the Bethe-ansatz scattering matrix without free parameters. Beyond the scattering probabilities, the col…
- arXiv · Published · First archived
A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians ↗
A scalable ground-state solver to be used in sample-based quantum diagonalization (SQD) is presented. The solver is built on JAX API, and is intended to be run on GPU platforms and possibly many-GPU high-performance computing facilities. It is capable of subspace diagonalization in up to $2^{30}$-dimensional subspace, with possible upgrades to enable even larger-scale operations. The two main components, a general-purpose eigensolver and an SQD-specific matrix-vector operation function, are described in detail. Efficiency of the eigensolver with respect to the GPU memory is demonstrated using…
- arXiv · Published · First archived
Quantum Query Complexity of Maximum Degree and its Generalizations ↗
We study the quantum query complexity of several basic graph problems. 1) We prove a tight $Θ(n^{3/2})$ bound for finding a maximum-out-degree vertex in an $n$-vertex tournament, resolving a conjecture of Mande, Paraashar, and Saurabh [FSTTCS'23]. 2) Using the same proof idea, we give a direct proof that finding a maximum-degree vertex in an $n$-vertex undirected graph requires $Θ(n^{3/2})$ quantum queries, a bound implicit in the work of Apers and Lee [CCC'21]. Both lower bounds above hold even for computing only the maximum degree or maximum out-degree. 3) We also study Maximum $(k,n-k)$-Cut…
- arXiv · Published · First archived
An Annealing-based Quantum Neural Network with Discrete Wavelet Transforms for Enhanced Air Pollution Nowcasting ↗
Air pollution poses a significant threat to public health, exacerbated by the rapid industrialization and urbanization of modern society. While traditional deep learning offers predictive capabilities, recent advancements in quantum machine learning (QML) present a compelling alternative. Here, we introduce a scalable hybrid framework that integrates variational quantum circuits with quantum annealing. Leveraging a neuromorphic quantum computing platform to transcend the qubit limitations of the NISQ era, we model particulate matter (PM$_{2.5}$) dynamics using high-density urban data from the…
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How to assess evidence about Physics
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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